Robust and portable capacity computing method for many finite element analyses of a high-fidelity crustal structure model aimed for coseismic slip estimation. (September 2016)

• Record Type: Journal Article
• Authors: Agata, Ryoichiro; Ichimura, Tsuyoshi; Hirahara, Kazuro; Hyodo, Mamoru; Hori, Takane; Hori, Muneo
• Is Part Of: Computers & geosciences. Volume 94(2016)
• Page Start: 121
• Page End: 130
• Publication Date: 2016-09
• Subjects: High-fidelity finite element model -- Crustal deformation -- Coseismic slip distribution estimation -- Inverse analysis -- Capacity computing -- The master–worker model -- Portability; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2016.05.015
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Computation of many Green's functions (GFs) in finite element (FE) analyses of crustal deformation is an essential technique in inverse analyses of coseismic slip estimations. In particular, analysis based on a high-resolution FE model (high-fidelity model) is expected to contribute to the construction of a community standard FE model and benchmark solution. Here, we propose a naive but robust and portable capacity computing method to compute many GFs using a high-fidelity model, assuming that various types of PC clusters are used. The method is based on the master–worker model, implemented using the Message Passing Interface (MPI), to perform robust and efficient input/output operations. The method was applied to numerical experiments of coseismic slip estimation in the Tohoku region of Japan; comparison of the estimated results with those generated using lower-fidelity models revealed the benefits of using a high-fidelity FE model in coseismic slip distribution estimation. Additionally, the proposed method computes several hundred GFs more robustly and efficiently than methods without the master–worker model and MPI. Abstract : Highlights: A coseismic slip estimation method using a high-fidelity crust model was proposed. Computation of many Green's functions (GFs) is essential for the inverse analyses. A robust and portable capacity computing method for many GFs was developed. The application example demonstrated the benefit of use of a high-fidelity model. The proposed method computed many GFs robustly and efficiently.